Bagging: the scikit-learn way

Let's now apply scikit-learn's BaggingClassifier to the Pokémon dataset.

You obtained an F1 score of around 0.63 with your custom bagging ensemble.

Will BaggingClassifier() beat it? Time to find out!

100 XP

Instantiate the base model, clf_dt: a "restricted" decision tree with a max depth of 4.
Build a bagging classifier using 21 estimators, with the decision tree as base estimator.
Predict the labels of the test set.